Next Article in Journal
An App about Healthy Habits as an Educational Resource during the Pandemic
Previous Article in Journal
Sleep Medication in Older Adults: Identifying the Need for Support by a Community Pharmacist
Correction published on 31 March 2022, see Healthcare 2022, 10(4), 657.
Article

An Explainable Machine Learning Approach for COVID-19’s Impact on Mood States of Children and Adolescents during the First Lockdown in Greece

1
University Mental Health Research Institute, 11527 Athens, Greece
2
First Psychiatric Department, Eginition Hospital, National and Kapodistrian University of Athens, 11528 Athens, Greece
3
Second Psychiatric Department, ‘Attikon’ University Hospital, National and Kapodistrian University of Athens, 12462 Athens, Greece
4
Department of Psychiatry, Faculty of Medicine, School of Health Sciences, University of Ioannina, 45110 Ioannina, Greece
5
Department of Child and Adolescent Psychiatry, Medical School, Democritus University of Thrace, University Hospital of Alexandroupolis, 68100 Alexandroupolis, Greece
6
Department of Child and Adolescent Psychiatry, Division of Psychiatry, ‘Asklepieion Voulas’ General Hospital, 16673 Attica, Greece
7
Hellenic Centre for Mental Health and Research, 10683 Athens, Greece
8
Athens Child and Adolescent Mental Health Centre, General Children’s Hospital ‘Pan. & Aglaia Kyriakou’, 11527 Athens, Greece
9
Mental Health Center, General Hospital ‘G. Hatzikosta’, 45445 Ioannina, Greece
10
Section of Clinical and Computational Psychiatry, National Institute of Mental Health, National Institutes of Health, Bethesda, MD 20892, USA
*
Author to whom correspondence should be addressed.
Academic Editor: Mario Miniati
Healthcare 2022, 10(1), 149; https://doi.org/10.3390/healthcare10010149
Received: 13 December 2021 / Revised: 7 January 2022 / Accepted: 10 January 2022 / Published: 13 January 2022 / Corrected: 31 March 2022
(This article belongs to the Topic Burden of COVID-19 in Different Countries)
The global spread of COVID-19 led the World Health Organization to declare a pandemic on 11 March 2020. To decelerate this spread, countries have taken strict measures that have affected the lifestyles and economies. Various studies have focused on the identification of COVID-19’s impact on the mental health of children and adolescents via traditional statistical approaches. However, a machine learning methodology must be developed to explain the main factors that contribute to the changes in the mood state of children and adolescents during the first lockdown. Therefore, in this study an explainable machine learning pipeline is presented focusing on children and adolescents in Greece, where a strict lockdown was imposed. The target group consists of children and adolescents, recruited from children and adolescent mental health services, who present mental health problems diagnosed before the pandemic. The proposed methodology imposes: (i) data collection via questionnaires; (ii) a clustering process to identify the groups of subjects with amelioration, deterioration and stability to their mood state; (iii) a feature selection process to identify the most informative features that contribute to mood state prediction; (iv) a decision-making process based on an experimental evaluation among classifiers; (v) calibration of the best-performing model; and (vi) a post hoc interpretation of the features’ impact on the best-performing model. The results showed that a blend of heterogeneous features from almost all feature categories is necessary to increase our understanding regarding the effect of the COVID-19 pandemic on the mood state of children and adolescents. View Full-Text
Keywords: COVID-19 pandemic; children and adolescents; machine learning; post hoc explainability; model calibration COVID-19 pandemic; children and adolescents; machine learning; post hoc explainability; model calibration
Show Figures

Graphical abstract

MDPI and ACS Style

Ntakolia, C.; Priftis, D.; Charakopoulou-Travlou, M.; Rannou, I.; Magklara, K.; Giannopoulou, I.; Kotsis, K.; Serdari, A.; Tsalamanios, E.; Grigoriadou, A.; Ladopoulou, K.; Koullourou, I.; Sadeghi, N.; O’Callaghan, G.; Lazaratou, E. An Explainable Machine Learning Approach for COVID-19’s Impact on Mood States of Children and Adolescents during the First Lockdown in Greece. Healthcare 2022, 10, 149. https://doi.org/10.3390/healthcare10010149

AMA Style

Ntakolia C, Priftis D, Charakopoulou-Travlou M, Rannou I, Magklara K, Giannopoulou I, Kotsis K, Serdari A, Tsalamanios E, Grigoriadou A, Ladopoulou K, Koullourou I, Sadeghi N, O’Callaghan G, Lazaratou E. An Explainable Machine Learning Approach for COVID-19’s Impact on Mood States of Children and Adolescents during the First Lockdown in Greece. Healthcare. 2022; 10(1):149. https://doi.org/10.3390/healthcare10010149

Chicago/Turabian Style

Ntakolia, Charis, Dimitrios Priftis, Mariana Charakopoulou-Travlou, Ioanna Rannou, Konstantina Magklara, Ioanna Giannopoulou, Konstantinos Kotsis, Aspasia Serdari, Emmanouil Tsalamanios, Aliki Grigoriadou, Konstantina Ladopoulou, Iouliani Koullourou, Neda Sadeghi, Georgia O’Callaghan, and Eleni Lazaratou. 2022. "An Explainable Machine Learning Approach for COVID-19’s Impact on Mood States of Children and Adolescents during the First Lockdown in Greece" Healthcare 10, no. 1: 149. https://doi.org/10.3390/healthcare10010149

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop